ArticleBMC medical genomics2012
Batch correction of microarray data substantially improves the identification of genes differentially expressed in rheumatoid arthritis and osteoarthritis.
Article in BMC medical genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
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- Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.Nanomaterials (Basel, Switzerland) · 2020Review
- DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration.Frontiers in genetics · 2020Article
- Merging microarray studies to identify a common gene expression signature to several structural heart diseases.BioData mining · 2020Article
- Screening responsive or resistant biomarkers of immune checkpoint inhibitors based on online databases.Frontiers of medicine · 2019Review
- Alternative empirical Bayes models for adjusting for batch effects in genomic studies.BMC bioinformatics · 2018Article
- Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer.Molecular medicine reports · 2018Article
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6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBatch effects due to sample preparation or array variation (type, charge, and/or platform) may influence the results of microarray experiments and thus mask and/or confound true biological differences. Of the published approaches for batch correction, the algorithm "Combating Batch Effects When Combining Batches of Gene Expression Microarray Data" (ComBat) appears to be most suitable for small sample sizes and multiple batches.
methodsSynovial fibroblasts (SFB; purity > 98%) were obtained from rheumatoid arthritis (RA) and osteoarthritis (OA) patients (n = 6 each) and stimulated with TNF-α or TGF-β1 for 0, 1, 2, 4, or 12 hours. Gene expression was analyzed using Affymetrix Human Genome U133 Plus 2.0 chips, an alternative chip definition file, and normalization by Robust Multi-Array Analysis (RMA). Data were batch-corrected for different acquiry dates using ComBat and the efficacy of the correction was validated using hierarchical clustering.
resultsIn contrast to the hierarchical clustering dendrogram before batch correction, in which RA and OA patients clustered randomly, batch correction led to a clear separation of RA and OA. Strikingly, this applied not only to the 0 hour time point (i.e., before stimulation with TNF-α/TGF-β1), but also to all time points following stimulation except for the late 12 hour time point. Batch-corrected data then allowed the identification of differentially expressed genes discriminating between RA and OA. Batch correction only marginally modified the original data, as demonstrated by preservation of the main Gene Ontology (GO) categories of interest, and by minimally changed mean expression levels (maximal change 4.087%) or variances for all genes of interest. Eight genes from the GO category "extracellular matrix structural constituent" (5 different collagens, biglycan, and tubulointerstitial nephritis antigen-like 1) were differentially expressed between RA and OA (RA > OA), both constitutively at time point 0, and at all time points following stimulation with either TNF-α or TGF-β1.
conclusionBatch correction appears to be an extremely valuable tool to eliminate non-biological batch effects, and allows the identification of genes discriminating between different joint diseases. RA-SFB show an upregulated expression of extracellular matrix components, both constitutively following isolation from the synovial membrane and upon stimulation with disease-relevant cytokines or growth factors, suggesting an "imprinted" alteration of their phenotype.
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